Time Series Model Selection

One way to think of a time series model is that it is an approximation to some unknown dynamic system. Akaike’s information criterion (AIC) is used for model selection, especially in the time series context. The author uses an example to discuss the problem of model selection and the use of model selection criteria. The most popular criteria are Akaike’s information criterion (AIC), Akaike's bias-corrected information criterion (AICC) suggested by Hurvich and Tsai, and the Bayesian information criterion (BIC) introduced by Schwarz. One approach to time series modeling is to fit a number of potential autoregressive moving average (ARMA) models to the data using the maximum likelihood estimation, choose a criterion, and select the model that has the best value according to this criterion. Impulse response functions are usually computed for stationary models. Controlled Vocabulary Terms Bayesian information criterion; maximum likelihood estimation; time series analysis